Introduction

When I set out to build a legal AI chatbot, I knew that accuracy was paramount. Legal queries require precise, contextual answers that a generic LLM might struggle with. That's where Retrieval-Augmented Generation (RAG) comes in.

What is RAG?

RAG combines the power of large language models with a retrieval system. Instead of relying solely on the model's training data, RAG:

1. Retrieves relevant documents from a knowledge base
2. Augments the prompt with this context
3. Generates a response based on both the query and retrieved information

RAG is particularly powerful for domain-specific applications where you need accurate, up-to-date information that wasn't in the model's training data.

The Tech Stack

For this project, I used:

• Python - Core language
• Faiss - Vector similarity search
• Google Gemini API - LLM for generation
• Streamlit - Web interface
• Kaggle Datasets - Legal document corpus

Implementation Steps

1. Document Processing

First, I processed the legal documents and created embeddings:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')

def create_embeddings(documents):
    embeddings = model.encode(documents)
    return embeddings

2. Building the Vector Index

Using Faiss for efficient similarity search:

import faiss
import numpy as np

def build_index(embeddings):
    dimension = embeddings.shape[1]
    index = faiss.IndexFlatL2(dimension)
    index.add(embeddings.astype('float32'))
    return index

3. Query Processing

When a user asks a question, we encode the query and find similar documents:

def search(query, index, documents, k=5):
    query_embedding = model.encode([query])
    distances, indices = index.search(query_embedding.astype('float32'), k)
    return [documents[i] for i in indices[0]]

Results

The chatbot achieved approximately 95% accuracy on legal queries, significantly reducing manual research time.

Key Learnings

1. Context window management - Be mindful of token limits
2. Chunk sizing - Smaller chunks = more precise retrieval
3. Prompt engineering - Clear instructions improve accuracy

Conclusion

RAG is a powerful technique for building domain-specific AI applications. By combining retrieval with generation, you get the best of both worlds: up-to-date, contextual information with natural language generation.